scholarly journals Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2574 ◽  
Author(s):  
Yeqi An ◽  
Yulin Zhou ◽  
Rongrong Li
Keyword(s):  
Power Generation ◽  
Electricity Generation ◽  
Moving Average ◽  
Probabilistic Methods ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

scholarly journals Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3278 ◽  
Author(s):  
Xinyu Han ◽  
Rongrong Li
Keyword(s):  
Energy Consumption ◽  
East Africa ◽  
Moving Average ◽  
Arima Model ◽  
Grey Model ◽  
Systematic Research ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

scholarly journals Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

2018 ◽  
Vol 10 (7) ◽  
pp. 2552 ◽  
Author(s):  
Minglu Ma ◽  
Min Su ◽  
Shuyu Li ◽  
Feng Jiang ◽  
Rongrong Li
Keyword(s):  
South Africa ◽  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Model ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Non Linear ◽  
Moving Average Model

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model

2012 ◽  
Vol 256-259 ◽  
pp. 2261-2265
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Moving Average ◽  
Structural Identification ◽  
Modal Parameters ◽  
Average Model ◽  
Identification Method ◽  
Moving Average Model ◽  
Nonlinear Relation ◽  
Auto Regressive

The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.

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